DC electronic load is a kind of instrument to replace the traditional dissipative resistance, which is widely used in the discharge performance test of DC power supply. However, it is difficult to achieve better control effect through analog circuit control in some test environments with high speed requirements for discharge test, and it is easy to produce overshoot. In digital circuits, however, in the actual control of DC electronic load, there are many uncertainties in the control system, such as interference and delay, which makes it difficult for the system to achieve the best control effect. This paper takes constant current mode as an example, in order to improve the effect of PID control, BP (Back Propagation) neural network is used to dynamically adjust the PID parameters online. After simulation analysis, under the ideal control environment, the adjustment time of BP-PID is only 0.0002s, which is 0.0005s higher than the traditional PID of 0.0007s. It can also reach a stable value within 0.0003s in the interference environment, with almost no overshoot. Therefore, compared with traditional PID control or analog circuit control, BP neural network PID control has better control effect on DC electronic load system.

Keren He, Liwei Jiang. Research on DC Electronic Load System Based on BP Neural Network PID Control. Academic Journal of Engineering and Technology Science (2022) Vol. 5, Issue 12: 56-61. https://doi.org/10.25236/AJETS.2022.051208.

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